• Bike Sharing Systems (BSS) is a very widely used method of transportation for many people worldwide.

• They have several advantages:

  • Non-polluting.
  • Circumvents traffic congestion.
  • Excellent for last-mile connections.
  • Have a convenient payment system.

• BSSs use self-serve bikes locked to docking stations.
• Bikes are unlocked, used for a trip, then left at the closest station to their destination.
• Bikes need to be relocated from low demand to high demand stations.

• The relocation process could be costly and time consuming.
• Stations with high demand cannot be identified in advance.
• Stations with high return rate cannot be identified in advance.
• Relocation may not be important if return rate is high enough to fill the demand.

The objective is to create multivariate regression models capable of estimating the following

• Demand prediction: Number of bikes needed at each individual station.
• Return prediction: Number of bikes returned at each individual station.

• Bike Renting:
  • Users return bikes to the same location they rented from.
  • Not a mode of transportation, used for leisure.
  • Exclusively a private business.
  • Not self-serve.
• Bike Sharing:
  • Users return bikes to a different station to the one they rented from.
  • Used primarily as a mode of transportation.
  • Government or institutionally supported.
  • Mostly self-serve.

• First Generation:
  • Unregulated free bikes, no parking or docking.
  • Failed due to stolen and damaged bikes.

• First Generation:
  • Unregulated free bikes, no parking or docking.
  • Failed due to stolen and damaged bikes.
• Second Generation:
  • Docked bikes paid with refundable coins.
  • Still in use in some countries.

• First Generation:
  • Unregulated free bikes, no parking or docking.
  • Failed due to stolen and damaged bikes.
• Second Generation:
  • Docked bikes paid with refundable coins.
  • Still in use in some countries.
• Third Generations:
  • The most common modern BSS.
  • Membership cards or payment through credit card on mobile app.

• First Generation:
  • Unregulated free bikes, no parking or docking.
  • Failed due to stolen and damaged bikes.
• Second Generation:
  • Docked bikes paid with refundable coins.
  • Still in use in some countries.
• Third Generations:
  • The most common modern BSS.
  • Membership cards or payment through credit card on mobile app.

• Fourth Generation:
  • Bikes have built in locks to prevent theft.
  • No need for docking, just use, finish trip, leave anywhere.

• Univariate Time Series:
  • Autoregressive moving Average (ARMA)
  • Autoregressive Integrated Moving Average (ARIMA)
• Multivariate Time Series:
  • Deep Learning with LSTM
  • Random Forest.
  • Boosting Algorithms.

• Demographics:

  • Wealth.
  • Average age.

• Community

• Weather

• Sliding Window Statistics:

  • Standard deviations.
  • Averages.

NB: Weather is especially important. BIXI BSS in Montreal is not operational in unfavorable weather, typically in mid-November to April.

• Mean Absolute Error (MAE): Good for calculating exact error value.

\[ \sum_{i=1}^{D}|x_i-y_i| \]

• Mean Square Error (MSE): Good for penalizing outliers. \[ \sum_{i=1}^{D}(x_i-y_i)^2 \]

• Root Mean Square Error (RMSE): A more interpretable version of MSE. \[ \sum_{i=1}^{D}\sqrt{(x_i-y_i)^2} \]

The data was provided by Lyft, the owner of the BSS company Baywheels (formally GoBike) operating in San Francisco Bay Area.

• The data is available as far as 2017 before Lyft’s acquisition of GoBike.
• The data used however is limited to 2021 and 2022 because:
  • The disruption caused by the 2020 COVID pandemic.
  • Potential business growth gap between 2019 and 2021.

• Weather data is very relevant in determining the demand.
• The weather data for San Francisco Bay Area is provided by Meteostat through a Python API.

Size:

• Original Number of Trips: 4744199 trips .
• Final Number of Trips: 3855197 trips.
• Number Of Stations: 535
• Period: From January 2021 to December 2022

Relevant Columns:

• Station Name (start/end)
• Time (start/end)
• Coordinates (start/end)
• Ride ID

• Station Standardization:

  • Stations in a particular street don’t always have the same coordinates.
  • Many stations don’t have a standardized name or ID, making them unidentifiable.
  • Solution To Reduce Data Loss:
    • Use a single coordinate for any identifiable stations.
    • Approximate the closest standard station to any trip.
    • If the closest station is less than 500 meters away, keep the trip, otherwise, drop.

• Same Station Trip:
  • Several trips usually take place in several minutes, with the end station being the same as start stations.
  • This could be a result of users trying out the system or changed minds.
  • To prevent redundancy, any same station trip with duration less than 4 minutes will be removed.

• Clustering:
  • Helps reduce weather data size by:
    • Approximating areas closest to each other.
    • Approximating the weather conditions for each cluster.
  • Separates the areas the BSS serves.

Definitions

• Demand: Number of trips starting at a particular station.
• Returns: Number of trips ending at a particular station.
• “Returns” does not indicate actual number of bikes available, but it indicates how many bikes finished their trips there, which when compared with demand should provide a good idea about the available bikes.
• Therefore, demand will be the main focus, while returns will be taken as secondary.

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